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Record W2197525297 · doi:10.1016/j.phrp.2015.11.012

Mediating and Moderating Effects in Ageism and Depression among the Korean Elderly: The Roles of Emotional Reactions and Coping Reponses

2015· article· en· W2197525297 on OpenAlexaff
Il‐Ho Kim, Samuel Noh, Heeran Chun

Bibliographic record

VenueOsong Public Health and Research Perspectives · 2015
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersNational Research Foundation of KoreaNational Research Foundation
KeywordsCoping (psychology)AngerPsychologySadnessClinical psychologySocial supportEmotional supportSocial psychology

Abstract

fetched live from OpenAlex

OBJECTIVES: This study evaluated the relationship between ageism and depression, exploring the stress-mediating and stress-moderating roles of emotional reactions and coping behaviors. METHODS: Data were from the 2013 Ageism and Health Study (n = 816), a cross-sectional survey of urban and rural community-dwelling seniors aged 60-89 years in South Korea. Participants with at least one experience of ageism reported on their emotional reactions and coping responses. The measure yielded two types of coping: problem-focused (taking formal action, confrontation, seeking social support) and emotion-focused (passive acceptance, emotional discharge). RESULTS: Although ageism was significantly associated with depressive symptoms (B = 0.27, p < 0.0001), the association was entirely mediated by emotional reactions such as anger, sadness, and powerlessness. Problem-focused coping, especially confrontation and social support, seemingly reduced the impact of emotional reactions on depression, whereas emotion-focused coping exacerbated the adverse effects. CONCLUSION: These findings support the cultural characterization explanation of ageism and related coping processes among Korean elderly and suggest that regulating emotional reactions may determine the efficacy of coping with ageism.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.136
GPT teacher head0.458
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations48
Published2015
Admission routes1
Has abstractyes

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